Searching for biomarkers in the fluidity of mental ill‐health
Authors: Antonio Verdejo‐Garcia
Journal: World Psychiatry (2023)

How close are we to establish biomarkers of psychiatric disorders? Still far, according to Abi-Dargham et al's review of the state of the art in some of the most prevalent and debilitating mental and neurodevelopmental conditions1. The authors provide a balanced appraisal of current evidence, which exposes the shortcomings of emerging biomarkers such as problems to capture between-subjects heterogeneity within psychiatric diagnoses, limited sensitivity and specificity, and the hindered transition from in-sample discovery to external and clinical validation stages. To change the landscape, they propose enabling adequately powered studies that are purpose-built for biomarker discovery (versus traditional case-control approaches), prioritizing long-term cohort studies supported by ad-hoc mechanistic experiments, and leveraging novel phenotype ontologies and digital tools to refine a priori stratification (i.e., to parse heterogeneity) and the reliability and validity of measurement. These are bold and sensible solutions, and I will highlight here potential enablers and additional challenges they may entail, drawing from examples stemming mostly from the substance use disorders literature, but also from recent studies on schizophrenia and bipolar disorder. A point that emerges from Abi-Dargham et al's review is that collaborative, multimodal studies (e.g., omics, multiple neuroimaging modalities, cognitive and psychosocial measures) including flexible prediction workflows are a promising approach to discover biomarkers for psychiatric disorders. There are encouraging examples of this methodology in the context of escalation of substance use. In a large-scale, multi-site European cohort study (the IMAGEN study), machine learning-based structural and functional neuroimaging indices of fronto-parietal executive function network alterations predicted escalation of binge drinking across middle adolescence both in-sample and using cross-validation2. This evidence can directly inform the design of time-sensitive and executive function-targeted prevention programs during this critical developmental period. Although the field of neuroscience-based prevention is still in an early stage, emerging interventions targeting executive control or using neuroscience-informed psychoeducation have shown positive initial results3. There is also an increasing effort to incorporate pragmatic, biologically-informed mechanistic measures of treatment response into clinical trials of cognitive training and remediation for substance use disorders. For example, electroencephalography indices of frontal midline theta changes, linked to executive function, have been successfully used as a marker of therapeutic response to mindfulness-based emotion regulation training for opioid misuse4. These mechanisms-informed trials have great potential to improve our understanding of the neurobiological signatures of active treatment ingredients, and thus inform development of predictive biomarkers for the treatment of psychiatric disorders. Emerging biomarker research will likely leverage on rapid technology advance. The neurobiological alterations that underpin several psychiatric conditions – such as schizophrenia, substance use, mood, anxiety, and stress-related disorders – hinge on the interaction between executive function, reward processing and emotion regulation networks. These networks can now be precisely measured and manipulated using human brain connectomics and non-invasive brain stimulation techniques respectively, as well as chemo- and opto-genetic tools for translational animal experiments. Studies using these novel techniques are generating detailed neural and neurochemical maps, and sophisticated insights into the valence and directionality of circuit-level alterations associated with mental disorders severity and progression5. This creates an opportunity for targeted, hypothesis-driven studies that investigate common and differential circuit-level alterations across different disorders. This targeted approach can contribute to expedite the efficiency and improve the specificity of brain biomarker discovery. At the behavioural level, digital technologies and advanced psychometric modelling are also improving the precision, specificity and validity of cognitive measures, which sit at the interface between the pinpointed neural networks and day-to-day behaviour and symptom expression6. These cognitive measures are better poised to detect individual differences in the cognitive mechanisms linked to the executive function, reward processing and emotion regulation networks, and thus can be exploited to refine behavioural phenotyping and improve a priori stratification approaches for biomarker discovery. But still, will we be close to the target? Key challenges are likely enduring. Mental disorders are multifactorial and fluid. Taking progression of alcohol use in the IMAGEN study as an example, while multimodal neuroimaging markers were significant contributors and optimized the overall prediction workflow, psychosocial life experiences such as romantic break-ups were the most influential predictor of escalation of consumption2. The pre-eminence of psychosocial over biological factors exposes the importance of integrating biomarkers with subjective experiences. In the future, brain computer interfaces could aid this integration by linking biological readouts with subjective reports in situ. This may contribute to disentangle between-subjects heterogeneity, as certain readouts could be meaningfully linked to influential psychosocial factors in some people but not others. In addition to between-subjects differences, there is increasing recognition of the relevance of high intra-individual variability in mental disorders. For example, people with substance use or bipolar disorders show substantial fluctuations in the neural systems and cognitive processes underpinning executive function, which are linked to meaningful clinical outcomes such as reduction of substance use or response to cognitive remediation interventions7. This phenomenon speaks to the importance of considering not only static but also dynamic biomarkers sensitive to state-related fluctuations in cognitive and neural networks function. It also suggests that, from a methodological standpoint, we should strategize development and harmonization of protocols that maximize test-retest reliability and enable tracking of dynamic biomarker trajectories that can reliably predict outcomes (and minimize error) across time. A related aspect of state-related variability is that, despite the scientific and intuitive appeal of mechanisms-driven predictive biomarkers, the efficacy of treatments for mental disorders may often rely on non-specific motivational factors. For example, cognitive bias modification (i.e., redirecting biases away from maladaptive cues) is a validated intervention for alcohol use disorder which has a clear-cut active mechanism involving a reduction of the appetitive value of alcohol-related stimuli. However, general treatment motivation (i.e., treatment seeking status) is a better predictor of the efficacy of the intervention than this specific active mechanism8. Another important challenge for establishing biomarkers of psychiatric disorders is the frequent presence of comorbidities. Notably, some of these comorbidities may impact the identification of biomarkers in unexpected ways: instead of amplifying the ability to detect maladaptive markers, they can obscure the detection of these markers. For example, some putative peripheral biomarkers of schizophrenia, such as pro-inflammatory interleukins and elevated levels of anandamide-related endocannabinoids, are not observed in people with schizophrenia and comorbid cannabis use disorder9. This finding highlights the malleable nature of biological markers in the context of highly prevalent comorbidities (i.e., substance use disorders) which co-occur not only with schizophrenia but also with several other neurodevelopmental and mental disorders. Altogether, the insights from the Abi-Dargham et al's review1 and the advances, limitations and opportunities discussed here suggest that, along with the excitement that novel approaches and technologies will likely bring to the biomarker discovery field, we need to remain mindful of the inherent challenges, particularly the biopsychosocial nature of psychiatric disorders and the multiple sources of inter- and intra-individual variability influencing clinical outcomes. We also need to work towards research endeavours that are better poised to address these challenges via collaboration between biological and psychosocial scientists, concerted large-scale international efforts to improve and harmonize both biological and behavioural measures (as well as their underpinning ontologies), and incorporation of people's living experiences into our definitions and measurements of biomarkers.